def run_day_of_month_analysis(self, strat):
from pythalesians.economics.seasonality.seasonality import Seasonality
from pythalesians.timeseries.calcs.timeseriescalcs import TimeSeriesCalcs
tsc = TimeSeriesCalcs()
seas = Seasonality()
strat.construct_strategy()
pnl = strat.get_strategy_pnl()
# get seasonality by day of the month
pnl = pnl.resample('B').mean()
rets = tsc.calculate_returns(pnl)
bus_day = seas.bus_day_of_month_seasonality(rets, add_average = True)
# get seasonality by month
pnl = pnl.resample('BM').mean()
rets = tsc.calculate_returns(pnl)
month = seas.monthly_seasonality(rets)
self.logger.info("About to plot seasonality...")
gp = GraphProperties()
pf = PlotFactory()
# Plotting spot over day of month/month of year
gp.color = 'Blues'
gp.scale_factor = self.SCALE_FACTOR
gp.file_output = self.DUMP_PATH + strat.FINAL_STRATEGY + ' seasonality day of month.png'
gp.html_file_output = self.DUMP_PATH + strat.FINAL_STRATEGY + ' seasonality day of month.html'
gp.title = strat.FINAL_STRATEGY + ' day of month seasonality'
gp.display_legend = False
gp.color_2_series = [bus_day.columns[-1]]
gp.color_2 = ['red'] # red, pink
gp.linewidth_2 = 4
gp.linewidth_2_series = [bus_day.columns[-1]]
gp.y_axis_2_series = [bus_day.columns[-1]]
pf.plot_line_graph(bus_day, adapter = self.DEFAULT_PLOT_ENGINE, gp = gp)
gp = GraphProperties()
gp.scale_factor = self.SCALE_FACTOR
gp.file_output = self.DUMP_PATH + strat.FINAL_STRATEGY + ' seasonality month of year.png'
gp.html_file_output = self.DUMP_PATH + strat.FINAL_STRATEGY + ' seasonality month of year.html'
gp.title = strat.FINAL_STRATEGY + ' month of year seasonality'
pf.plot_line_graph(month, adapter = self.DEFAULT_PLOT_ENGINE, gp = gp)
return month
from pythalesians.market.requests.timeseriesrequest import TimeSeriesRequest
# process data
from pythalesians.economics.seasonality.seasonality import Seasonality
from pythalesians.timeseries.calcs.timeseriescalcs import TimeSeriesCalcs
# displaying data
from pythalesians.graphics.graphs.plotfactory import PlotFactory
from pythalesians.graphics.graphs.graphproperties import GraphProperties
# logging
from pythalesians.util.loggermanager import LoggerManager
import datetime
seasonality = Seasonality()
tsc = TimeSeriesCalcs()
logger = LoggerManager().getLogger(__name__)
pf = PlotFactory()
###### calculate seasonal moves in EUR/USD and GBP/USD (using Quandl data)
if True:
time_series_request = TimeSeriesRequest(
start_date = "01 Jan 1970", # start date
finish_date = datetime.date.today(), # finish date
freq = 'daily', # daily data
data_source = 'quandl', # use Quandl as data source
tickers = ['EURUSD', # ticker (Thalesians)
'GBPUSD'],
fields = ['close'], # which fields to download
from pythalesians.market.requests.timeseriesrequest import TimeSeriesRequest
# process data
from pythalesians.economics.seasonality.seasonality import Seasonality
from pythalesians.timeseries.calcs.timeseriescalcs import TimeSeriesCalcs
# displaying data
from pythalesians.graphics.graphs.plotfactory import PlotFactory
from pythalesians.graphics.graphs.graphproperties import GraphProperties
# logging
from pythalesians.util.loggermanager import LoggerManager
import datetime
seasonality = Seasonality()
tsc = TimeSeriesCalcs()
logger = LoggerManager().getLogger(__name__)
pf = PlotFactory()
# just change "False" to "True" to run any of the below examples
###### calculate seasonal moves in EUR/USD and GBP/USD (using Quandl data)
if True:
time_series_request = TimeSeriesRequest(
start_date="01 Jan 1970", # start date
finish_date=datetime.date.today(), # finish date
freq='daily', # daily data
data_source='quandl', # use Quandl as data source
tickers=[
